Approximating a Wavefunction as an Unconstrained Sum of Slater Determinants

The wavefunction for the multiparticle Schr\"odinger equation is a function of many variables and satisfies an antisymmetry condition, so it is natural to approximate it as a sum of Slater determinants. Many current methods do so, but they impose additional structural constraints on the determinants, such as orthogonality between orbitals or an excitation pattern. We present a method without any such constraints, by which we hope to obtain much more efficient expansions, and insight into the inherent structure of the wavefunction. We use an integral formulation of the problem, a Green's function iteration, and a fitting procedure based on the computational paradigm of separated representations. The core procedure is the construction and solution of a matrix-integral system derived from antisymmetric inner products involving the potential operators. We show how to construct and solve this system with computational complexity competitive with current methods.Comment: 30 page

Signs of subclinical coronary atherosclerosis in relation to risk factor distribution in the Multi-Ethnic Study of Atherosclerosis (MESA) and the Heinz Nixdorf Recall Study (HNR)

AIMS: Modern imaging technology allows us the visualization of coronary artery calcification (CAC), a marker of subclinical coronary atherosclerosis. The prevalence, quantity, and risk factors for CAC were compared between two studies with similar imaging protocols but different source populations: the Multi-Ethnic Study of Atherosclerosis (MESA) and the Heinz Nixdorf Recall Study (HNR). METHODS AND RESULTS: The measured CAC in 2220 MESA participants were compared with those in 3,126 HNR participants with the inclusion criteria such as age 45-75 years, Caucasian race, and free of baseline cardiovascular disease. Despite similar mean levels of CAC of 244.6 among participants in MESA and of 240.3 in HNR (P = 0.91), the prevalence of CAC > 0 was lower in MESA (52.6%) compared with HNR (67.0%) with a prevalence rate ratio of CAC > 0 of 0.78 [95% confidence interval (CI): 0.72-0.85] after adjustment for known risk factors. Consequently, among participants with CAC > 0, the participants in MESA tended to have higher levels of CAC than those in HNR (ratio of CAC levels: 1.39; 95% CI: 1.19-1.63), since many HNR participants have small (near zero) CAC values. CONCLUSIONS: The CAC prevalence was lower in the United States (MESA) cohort than in the German (HNR) cohort, which may be explained by more favourable risk factor levels among the MESA participants. The predictors for increased levels of CAC were, however, similar in both cohorts with the exception that male gender, blood pressure, and body mass index were more strongly associated in the HNR cohort

Atherosclerotic pattern of coronary myocardial bridging assessed with CT coronary angiography

The aim of our study was to evaluate the atherosclerotic pattern of patients with coronary myocardial bridging (MB) by means of CT Coronary Angiography (CT-CA). 254 consecutive patients (166 male, mean age 58.6 ± 10.3) who underwent 64-slice CT-CA according to current clinical indications were reviewed for the presence of MB and concomitant segmental atherosclerotic pattern. Coronary plaques were assessed in all patients enrolled. 73 patients (29%) presented single (90%) or multiple (10%) MB, frequently (93%) localized in the mid-distal left anterior descending artery. The MB segment was always free of atherosclerosis. Segments proximal to the MB presented: no atherosclerotic disease (n = 37), positive remodeling (n = 23), 50% stenoses (n = 7). Distal segments presented a different atherosclerosis pattern (P < 0.0001): absence of disease (n = 73), no significant lesions (n = 8). No significant differences were found between segments proximal to MB and proximal coronary segments apart from left main trunk. Pattern of atherosclerotic lesions located in segments 6 and 7 significantly differs between patients with MB and patients without MB (P < 0.05). CT-CA is a reliable method to non-invasively demonstrate MB and related atherosclerotic pattern. CT-CA provides new insight regarding atherosclerosis distribution in segments close to MB

The Female Athlete's Heart: Facts and Fallacies.

Purpose of the review For many years, competitive sport has been dominated by men. Recent times have witnessed a significant increase in women participating in elite sports. As most studies investigated male athletes, with few reports on female counterparts, it is crucial to have a better understanding on physiological cardiac adaptation to exercise in female athletes, to distinguish normal phenotypes from potentially fatal cardiac diseases. This review reports on cardiac adaptation to exercise in females. Recent findings Recent studies show that electrical, structural, and functional cardiac changes due to physiological adaptation to exercise differ in male and female athletes. Women tend to exhibit eccentric hypertrophy, and while concentric hypertrophy or concentric remodeling may be a normal finding in male athletes, it should be evaluated carefully in female athletes as it may be a sign of pathology. Although few studies on veteran female athletes are available, women seem to be affected by atrial fibrillation, coronary atherosclerosis, and myocardial fibrosis less than male counterparts. Summary Males and females exhibit many biological, anatomical, and hormonal differences, and cardiac adaptation to exercise is no exception. The increasing participation of women in sports should stimulate the scientific community to develop large, longitudinal studies aimed at a better understanding of cardiac adaptation to exercise in female athletes

Outcome of coronary plaque burden: a 10-year follow-up of aggressive medical management

<p>Abstract</p> <p>Background</p> <p>The effect of aggressive medical therapy on quantitative coronary plaque burden is not generally known, especially in ethnic Chinese.</p> <p>Aims</p> <p>We reasoned that Cardiac CT could conveniently quantify early coronary atherosclerosis in our patient population, and hypothesized that serial observation could differentiate the efficacy of aggressive medical therapy regarding progression and regression of the atherosclerotic process, as well as evaluating the additional impact of life-style modification and the relative effects of the application of statin therapy.</p> <p>Methods</p> <p>We employed a standardized Cardiac CT protocol to serially scan 113 westernized Hong Kong Chinese individuals (64 men and 49 women) with Chest Pain and positive coronary risk factors. In all cases included for this serial investigation, subsequent evaluation showed no significantly-obstructive coronary disease by functional studies and angiography. After stringent risk factor modification, including aggressive statin therapy to achieve LDL-cholesterol lowering conforming to N.C.E.P. ATP III guidelines, serial CT scans were performed 1-12 years apart for changes in coronary artery calcification (CAC), using the Agatston Score (AS) for quantification.</p> <p>Results</p> <p>At baseline, the mean AS was 1413.6 for males (mean age 54.4 years) and 2293.3 for females (mean age 62.4 years). The average increase of AS in the entire study population was 24% per year, contrasting with 16.4% per year on strict risk factor modification plus statin therapy, as opposed to 33.2% per year for historical control patients (p < 0.001). Additionally, 20.4% of the 113 patients demonstrated decreasing calcium scores. Medical therapy also yielded a remarkably low adverse event rate during the follow-up period --- 2 deaths, 2 strokes and only 1 case requiring PCI.</p> <p>Conclusions</p> <p>This study revealed that aggressive medical therapy can positively influence coronary plaque aiding in serial regression of calcium scores.</p

Accuracy of advanced versus strictly conventional 12-lead ECG for detection and screening of coronary artery disease, left ventricular hypertrophy and left ventricular systolic dysfunction

<p>Abstract</p> <p>Background</p> <p>Resting conventional 12-lead ECG has low sensitivity for detection of coronary artery disease (CAD) and left ventricular hypertrophy (LVH) and low positive predictive value (PPV) for prediction of left ventricular systolic dysfunction (LVSD). We hypothesized that a ~5-min resting 12-lead <it>advanced </it>ECG test ("A-ECG") that combined results from both the advanced and conventional ECG could more accurately screen for these conditions than strictly conventional ECG.</p> <p>Methods</p> <p>Results from nearly every conventional and advanced resting ECG parameter known from the literature to have diagnostic or predictive value were first retrospectively evaluated in 418 healthy controls and 290 patients with imaging-proven CAD, LVH and/or LVSD. Each ECG parameter was examined for potential inclusion within multi-parameter A-ECG scores derived from multivariate regression models that were designed to optimally screen for disease in general or LVSD in particular. The performance of the best retrospectively-validated A-ECG scores was then compared against that of optimized pooled criteria from the strictly conventional ECG in a test set of 315 additional individuals.</p> <p>Results</p> <p>Compared to optimized pooled criteria from the strictly conventional ECG, a 7-parameter A-ECG score validated in the training set increased the sensitivity of resting ECG for identifying disease in the test set from 78% (72-84%) to 92% (88-96%) (P < 0.0001) while also increasing specificity from 85% (77-91%) to 94% (88-98%) (P < 0.05). In diseased patients, another 5-parameter A-ECG score increased the PPV of ECG for LVSD from 53% (41-65%) to 92% (78-98%) (P < 0.0001) without compromising related negative predictive value.</p> <p>Conclusion</p> <p>Resting 12-lead A-ECG scoring is more accurate than strictly conventional ECG in screening for CAD, LVH and LVSD.</p

withdrawn 2017 hrs ehra ecas aphrs solaece expert consensus statement on catheter and surgical ablation of atrial fibrillation

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Numerical operator calculus in higher dimensions

When an algorithm in dimension one is extended to dimension d, in nearly every case its computational cost is taken to the power d. This fundamental difficulty is the single greatest impediment to solving many important problems and has been dubbed the curse of dimensionality. For numerical analysis in dimension d, we propose to use a representation for vectors and matrices that generalizes separation of variables while allowing controlled accuracy. Basic linear algebra operations can be performed in this representation using one-dimensional operations, thus bypassing the exponential scaling with respect to the dimension. Although not all operators and algorithms may be compatible with this representation, we believe that many of the most important ones are. We prove that the multiparticle Schrödinger operator, as well as the inverse Laplacian, can be represented very efficiently in this form. We give numerical evidence to support the conjecture that eigenfunctions inherit this property by computing the ground-state eigenfunction for a simplified Schrödinger operator with 30 particles. We conjecture and provide numerical evidence that functions of operators inherit this property, in which case numerical operator calculus in higher dimensions becomes feasible

Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology

There are currently 85,000 chemicals registered with the Environmental Protection Agency (EPA) under the Toxic Substances Control Act, but only a small fraction have measured toxicological data. To address this gap, high-throughput screening (HTS) and computational methods are vital. As part of one such HTS effort, embryonic zebrafish were used to examine a suite of morphological and mortality endpoints at six concentrations from over 1,000 unique chemicals found in the ToxCast library (phase 1 and 2). We hypothesized that by using a conditional generative adversarial network (cGAN) or deep neural networks (DNN), and leveraging this large set of toxicity data we could efficiently predict toxic outcomes of untested chemicals. Utilizing a novel method in this space, we converted the 3D structural information into a weighted set of points while retaining all information about the structure. In vivo toxicity and chemical data were used to train two neural network generators. The first was a DNN (Go-ZT) while the second utilized cGAN architecture (GAN-ZT) to train generators to produce toxicity data. Our results showed that Go-ZT significantly outperformed the cGAN, support vector machine, random forest and multilayer perceptron models in crossvalidation, and when tested against an external test dataset. By combining both Go-ZT and GAN-ZT, our consensus model improved the SE, SP, PPV, and Kappa, to 71.4%, 95.9%, 71.4% and 0.673, respectively, resulting in an area under the receiver operating characteristic (AUROC) of 0.837. Considering their potential use as prescreening tools, these models could provide in vivo toxicity predictions and insight into the hundreds of thousands of untested chemicals to prioritize compounds for HT testing